(Reminder: Don’t forget to utilize the concept maps and
study questions as you study this and the other chapters.)

The experimental research designs discussed in this chapter are used when it is
impossible to randomly assign participants to comparison groups
(quasi-experimental designs) and when a researcher is faced with a situation
where only one or two participants can participate in the research study
(single case designs).

Like
the designs in the last chapter, quasi-experimental and single-case
designs do have manipulation of the independent variable (otherwise
they would not be “experimental research” designs).

Quasi-Experimental
Research Designs

These are designs that are used when it is not possible to
control for all potentially confounding variables; in most cases this is
because the participants cannot be randomly assigned to the groups.

Causal
explanations can be made when using quasi-experimental designs but only
when you collect data that demonstrate that plausible rival explanations
are unlikely, and the evidence will still not be as strong as with one of
the strong designs discussed in the last chapter.

You
can view quasi-experiments as falling in the center of a continuum with
weak experimental designs on the far left side and strong experimental
designs on the far right side.(In
other words, quasi designs are not the worst and they are not the best.
They are in-between or moderately strong designs.)

Three
quasi-experimental research designs are presented in the text: the
nonequivalent comparison-group design, the interrupted time-series design,
and the regression discontinuity design.

Nonequivalent Comparison-Group Design

This is a design that contains a treatment group and a
nonequivalent untreated comparison group about of which are administered
pretest and posttest measures. The groups are “nonequivalent” because you lack
random assignment (although there are some control techniques that can help
make the groups similar such as matching and statistical control). Because of
the lack of random assignment, there is no assurance that the groups are highly
are similar at the outset of the study.

Here is a depiction of the nonequivalent comparison-group
design:

Because
there is no random assignment to groups, confounding variables (rather
than the independent variable) may explain any difference observed between
the experimental and control groups.

The
most common threat to the internal validity of this type of design is
differential selection. The problem is that the groups may be different on
many variables that are also related to the dependent variable (e.g., age,
gender, IQ, reading ability, attitude, etc.).

Here
is a list of all of the primary threats to this design.

It is a good idea to collect data that
can be used to demonstrate that key confounding variables are not the
cause of the obtained results. Hence, you will need to think about
potential rival explanations during the planning phase of your research
study so that you can collect the necessary data to control for these
factors.

You
can eliminate the influence of many confounding variables by using the
various control techniques, especially statistical control (where you
measure the confounding variables at the pretest and control for them
using statistical procedures after the study has been completed) and matching
(where you select people to be in the groups so that the members in the
different groups are similar on the matching variables).

Only
when you can rule out the effects of confounding variables can you
confidently attribute the observed group difference at the posttest to the
independent variable.

Interrupted Time-Series Design

This is a design in which a treatment condition is accessed
by comparing the pattern of pretest responses with the pattern of posttest
responses obtained from a single group of participants. In other words, the
participants are pretested a number of times and then posttested a number of
times after or during exposure to the treatment condition.

Here is a depiction of the interrupted time-series design:

The
pretesting phase is called the baseline which refers to the
observation of a behavior prior to the presentation of any treatment
designed to alter the behavior of interest.

A
treatment effect is demonstrated only if the pattern of posttreatment
responses differs from the pattern of pretreatment responses. That is, the
treatment effect is demonstrated by a discontinuity in the pattern of
pretreatment and posttreatment responses.

For
example, an effect is demonstrated when there is a change in the level
and/or slope of the posttreatment responses as compared to the
pretreatment responses.

Here
is an example where both the level and slope changed during the
intervention:

·Many confounding variables are ruled out in the
interrupted time-series design because they are present in both the
pretreatment and posttreatment responses (i.e., the pretreatment and
posttreatment responses will not differ on most confounding variables).

·However, the main potentially confounding variable that
cannot be ruled out is a history effect. The history threat is a plausible
rival explanation if some event other than the treatment co-occurs with the
onset of the treatment.

Bonus material (not required)

Although not discussed in the text, there is an extension of
the interrupted time-series design. It is called the multiple time-series
design—it is the basic interrupted time-series design with a comparable
control group added to it. I mention this design because I do want you to
remember that YOU can put together different designs simply by using different
combinations of pretests, posttests, different types of groups, varying the
number of pretests and posttests, using a control group or not, including more
than one outcome variable, and so forth.

Both
the experimental and control groups are repeatedly pretested in the
multiple time-series design. Then the experimental group receives the
treatment and the control group receives some standard treatment or no
treatment, and, finally, both groups are repeatedly posttested.

Here
is a picture of the multiple time-series design:

Including
a control group provides control for the history effect, but only if the
different groups are truly comparable and any history effect influences
both groups to the same degree (i.e., as long as you don't have a
selection-history effect). The various additive and interactive effects
remain as potential threats to this design.

Regression Discontinuity Design

This is a design that is used to access the effect of a
treatment condition by looking for a discontinuity in regression lines between
individuals who score lower and higher than some predetermined cutoff score on
an assignment variable.

Here
is the depiction of the design:

For
example you might use a standardized test as your assignment variable, set
the cutoff at 50, and administer the treatment to those falling at 50 or
higher and use those with scores lower that 50 as your control group.

This
is actually quite a strong design, and methodologists have, for a number
of years, been trying to get researcher to use this design more
frequently.

One
uses statistical techniques to control for differences on the assignment
variable and then checks to see whether the groups significantly differ.

Here is an example where a difference or “discontinuity” is
easily seen:

If
you cannot assign the participants to the treatment condition based on
their assignment variable scores, you will not be able to use this design.
On the other hand, if you can do this, then this is an excellent design.

Single-Case
Experimental Designs

These are designs where the researcher attempts to
demonstrate an experimental treatment effect using single participants, one at
a time.

The A-B-A design is a design in which the participant
is repeatedly pretested (the first A phase or baseline condition), then the
experimental treatment condition is administered and the participant is
repeatedly posttested (the B phase or treatment phase). Following the
posttesting stage, the pretreatment conditions are reinstated and the participant
is again repeatedly tested on the dependent variable (the second A phase or the
return to baseline condition).

Here
is a depiction of the A-B-A design:

The
effect of the experimental treatment is demonstrated if the pattern of the
pre- and posttreatment responses ( the first A phase and the B phase)
differ and the pattern of responses reverts back to the
original pretreatment level when the pretreatment conditions are
reinstated (the second A or return to baseline phase).

Including
the second A phase controls for the potential rival hypothesis of history
that is a problem in a basic time series design (i.e., in an A-B design).

Basically,
you are looking for the "fingerprint" of a stable baseline
(during the first A phase), then a clear jump or change in level or slope
(during the B phase), and then a clear reversal or return to the stable
baseline (during the second A phase).

For
example, if you hope for low values on your dependent measure (e.g.,
talking out behavior), you would hope to see a high-low-high pattern.

Conversely,
if you hope for high values on your dependent measure (e.g., attending to
what the teacher says), you would hope to see a low-high- low pattern.

One
limitation of the A-B-A design is that it ends with baseline condition or
the withdrawal of the treatment condition so the participant does not
receive the benefit of the treatment condition at the end of the
experiment.

This
limitation can be overcome by including a fourth phase which adds a second
administration of the treatment condition so the design becomes an A-B-A-B
design.

A
limitation of both the A-B-A and the A-B-A-B designs is that they are
dependent on the pattern of responses reverting to baseline conditions
when the experimental treatment condition is withdrawn. This may not occur
if the experimental treatment is so powerful that its effect continues
even when the treatment is withdrawn.

If a
reversal to baseline conditions does not occur another design (such as the
multiple-baseline design) must be used to demonstrate the effectiveness of
the treatment condition.

Multiple-Baseline Design

This is a design that investigates two or more people,
behaviors, or settings to identify the effect of an experimental treatment. The
key is that the treatment condition is successively administered to the
different people, behaviors, or settings.

·Here is a depiction of the design:

·The multiple-baseline design requires that baseline
behavior is collected on the several people, behaviors, or settings and then
the experimental treatment is successively administered to the people,
behaviors, or settings.

·The experimental treatment effect is demonstrated if a
change in response occurs when the treatment is administered to each person,
behavior, or setting (i.e., when the fingerprint you are looking for is
observed).

·Here is an example where a treatment fingerprint is
easily seen:

Rival
hypotheses are unlikely to account for the changes in the behavior if the
behavior change only occurs after the treatment effect is administered to
each successive person, behavior, or setting.

This
design avoids the problem of failure to revert to baseline that can exist
with the A-B-A and A-B-A-B designs.

Changing-Criterion Design

This is a single-case design that is used when a behavior
needs to be shaped over time or when it is necessary to gradually change a
behavior through successive treatment periods to reach a desired criterion.

This
design involves collecting baseline data on the target behavior and then
administering the experimental treatment condition across a series of
intervention phases where each intervention phase uses a different
criterion of successful performance until the desired criterion is
reached.

The criterion used in each successive
intervention phase should be large enough to detect a change in behavior
but small enough so that it can be achieved.

Here
is an example this design.

Methodological
Considerations in
Using Single-Case Designs

The following table presents some major methodological
issues you must consider when using single-case designs.